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Julien Mairal - Publications by Topic
This page on publications by topics is not updated anymore. Check the list per year.
My publications are also available on my Google Scholar profile.
Review articles and monographs
J. Mairal, F. Bach and J. Ponce. Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Graphics and Vision. volume 8(2-3), pages 85–283, 2014. (project page and software coming soon).
F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Optimization with Sparsity-Inducing Penalties. Foundations and Trends in Machine Learning, 4(1), pages 1–106, 2012. source code.
F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Structured Sparsity through Convex Optimization. Statistical Science, 27(4), pages 450-468, 2012. source code.
F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Convex Optimization with Sparsity-Inducing Norms. In S. Sra, S. Nowozin, S. J. Wright., editors, Optimization for Machine Learning, MIT Press 2011. source code.
J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S Huang and S. Yan. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, 98(6), pages 1031–1044, 2010.
Large-scale optimization for machine learning
M. Arbel and J. Mairal. Non-Convex Bilevel Games with Critical Point Selection Maps. to appear at NeurIPS. 2022.
H. Zenati, A. Bietti, E. Diemert, J. Mairal, M. Martin and P. Gaillard. Efficient Kernel UCB for Contextual Bandits.. International Conference on Artificial Intelligence and Statistics (AISTATS). 2022.
M. Arbel and J. Mairal. Amortized Implicit Differentiation for Stochastic Bilevel Optimization. International Conference on Learning Representations (ICLR). 2022.
J. Mairal. Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C, and soon more. arXiv.1912.08165. 2019. source code
G. Mialon, A. d'Aspremont and J. Mairal. Screening Data Points in Empirical Risk Minimization via
Ellipsoidal Regions and Safe Loss Functions. International Conference on Artificial Intelligence and Statistics (AISTATS). 2020. source code
A. Kulunchakov and J. Mairal. Estimate Sequences for Stochastic Composite Optimization:
Variance Reduction, Acceleration, and Robustness to Noise. Journal of Machine Learning Research (JMLR) 21(155), pages 1–52, 2020.
A. Kulunchakov and J. Mairal. A Generic Acceleration Framework for Stochastic Composite Optimization. Adv. Neural Information Processing Systems (NeurIPS). 2019.
A. Kulunchakov and J. Mairal. Estimate Sequences for Variance-Reduced Stochastic Composite Optimization. International Conference on Machine Learning (ICML). 2019.
H. Lin, J. Mairal and Z. Harchaoui. An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration. SIAM Journal on Optimization. 29(2), pages 1408–1443, 2019. source code
C. Paquette, H. Lin, D. Drusvyatskiy, J. Mairal, Z. Harchaoui. Catalyst for Gradient-Based Non-Convex Optimization. International Conference on Artificial Intelligence and Statistics (AISTATS). 2018.
H. Lin, J. Mairal and Z. Harchaoui. Catalyst Acceleration for First-order
Convex Optimization: from Theory to Practice. Journal of Machine Learning Research (JMLR). 18(212), pages 1–54, 2018. source code
C. Paquette, H. Lin, D. Drusvyatskiy, J. Mairal, Z. Harchaoui. Catalyst Acceleration for Gradient-Based Non-Convex Optimization. preprint arXiv:1703.10993. 2018. (long version of the AISTATS paper above).
A. Bietti and J. Mairal. Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure. Adv. Neural Information Processing Systems (NIPS). 2017. source code
H. Lin, J. Mairal, and Z. Harchaoui. A Universal Catalyst for First-Order Optimization. Adv. Neural Information Processing Systems (NIPS). 2015.
J. Mairal. Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning. SIAM Journal on Optimization. volume 25, number 2, pages 829–855, 2015. source code. scripts for reproducing the figures.
J. Mairal. Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization. Adv. Neural Information Processing Systems (NIPS). 2013. source code.
J. Mairal. Optimization with First-Order Surrogate Functions. International Conference on Machine Learning (ICML), 2013. source code.
Deep kernel machines
M. Choraria, L. T. Dadi, G. Chrysos, J. Mairal and V. Cevher. The Spectral Bias of Polynomial Neural Networks. International Conference on Learning Representations (ICLR). 2022.
G. Mialon, D. Chen, M. Selosse, and J. Mairal. GraphiT: Encoding Graph Structure in Transformers. preprint arXiv:2106.05667. 2021. source code
G. Mialon, D. Chen, A. d'Aspremont and J. Mairal. A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention. International Conference on Learning Representations (ICLR). 2021. source code
D. Chen, L. Jacob and J. Mairal. Convolutional Kernel Networks for Graph-Structured Data. International Conference on Machine Learning (ICML). 2020. source code
D. Chen, L. Jacob and J. Mairal. Recurrent Kernel Networks. Adv. Neural Information Processing Systems (NeurIPS). 2019. source code
A. Bietti and J. Mairal. On the Inductive Bias of Neural Tangent Kernels. Adv. Neural Information Processing Systems (NeurIPS). 2019.
A. Bietti, G. Mialon, D. Chen, and J. Mairal. A Kernel Perspective for Regularizing Deep Neural Networks. International Conference on Machine Learning (ICML). 2019. source code
A. Bietti and J. Mairal. Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations. Journal of Machine Learning Research (JMLR). 20(25), pages 1–49, 2019.
A. Bietti and J. Mairal. Invariance and Stability of Deep Convolutional Representations. Adv. Neural Information Processing Systems (NIPS). 2017.
J. Mairal. End-to-End Kernel Learning with Supervised Convolutional Kernel Networks. Adv. Neural Information Processing Systems (NIPS), 2016. source code . Errata .
M. Paulin, J. Mairal, M. Douze, Z. Harchaoui, F. Perronnin, and C. Schmid. Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach. International Journal of Computer Vision (IJCV). 121(1), pages 149–168, 2017. project page + source code.
M. Paulin, M. Douze, Z. Harchaoui, J. Mairal, F. Perronnin, and C. Schmid. Local Convolutional Features with Unsupervised Training for Image Retrieval. International Conference on Computer Vision (ICCV), 2015. project page + source code.
J. Mairal, P. Koniusz, Z. Harchaoui and C. Schmid. Convolutional Kernel Networks. Adv. Neural Information Processing Systems (NIPS). 2014. The project page with the source code.
Data-Efficient Computer Vision
E. Fini, V. G. Turrisi da Costa, X. Alameda-Pineda, E. Ricci, K. Alahari and J. Mairal. Self-Supervised Models are Continual Learners. to appear at the conference on Computer Vision and Pattern Recognition (CVPR). 2022.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski and A. Joulin. Emerging Properties in Self-Supervised Vision Transformers. International Conference on Computer Vision (ICCV). 2021. source code
M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, A. Joulin. Unsupervised Learning of Visual Features by Contrasting Cluster Assignments. Adv. Neural Information Processing Systems (NeurIPS). 2020. source code
N. Dvornik, C. Schmid and J. Mairal. Selecting Relevant Features from a Multi-Domain Representation for Few-shot Classification.
European Conference on Computer Vision (ECCV). 2020. source code
M. Caron, A. Morcos, P. Bojanowski, J. Mairal and A. Joulin. Pruning Convolutional Neural Networks with Self-Supervision. preprint arXiv:2001.03554. 2020.
M. Caron, P. Bojanowski, J. Mairal and A. Joulin. Unsupervised Pre-Training of Image Features on Non-Curated Data. International Conference on Computer Vision (ICCV). 2019. source code .
N. Dvornik, C. Schmid and J. Mairal. Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. International Conference on Computer Vision (ICCV). 2019. source code .
M. Dvornik, J. Mairal and C. Schmid. On the Importance of Visual Context for Data Augmentation in Scene Understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2019. source code
M. Dvornik, J. Mairal and C. Schmid. Modeling Visual Context is Key to Augmenting Object Detection Datasets. European Conference on Computer Vision (ECCV). 2018. source code
H. O. Song, R. Girshick, S. Jegelka, J. Mairal, Z. Harchaoui and T. Darrell. On learning to localize objects with minimal supervision. International Conference on Machine Learning (ICML), 2014. source code.
Misc. Machine learning and computer vision
G. Beugnot, J. Mairal, and A. Rudi. On the Benefits of Large Learning Rates for Kernel Methods. preprint arXiv:2202.13733. 2022.
H. Zenati, A. Bietti, M. Martin, E. Diemert and J. Mairal. Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline Evaluation. preprint arXiv:2004.11722. 2021. source code
G. Beugnot, J. Mairal, and A. Rudi. Beyond Tikhonov: Faster Learning with Self-Concordant Losses via Iterative Regularization. Adv. Neural Information Processing Systems (NeurIPS). 2021.
D. Wynen, C. Schmid and J. Mairal.
Unsupervised Learning of Artistic
Styles with Archetypal Style Analysis. Adv. Neural Information Processing Systems (NeurIPS). 2018. project page
N. Dvornik, K. Shmelkov, J. Mairal and C. Schmid. BlitzNet: A Real-Time Deep Network for Scene Understanding. International Conference on Computer Vision (ICCV), 2017. source code
A. Cherian, J. Mairal, K. Alahari and C. Schmid. Mixing Body-Part Sequences for Human Pose Estimation IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. project page, source code, dataset.
J. Mairal, R. Keriven, and A. Chariot. Fast and efficient dense variational stereo on GPU. In 3rd International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), 2006.
Bioinformatics
D. Chen, L. Jacob, and J. Mairal. Biological Sequence Modeling with Convolutional Kernel Networks. Bioinformatics, volume 35, issue 18, pages 3294-3302, 2019. also accepted at RECOMB 2019. source code
T. Dias-Alves, J. Mairal, and M. Blum. Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species. Molecular Biology and Evolution (MBE), volume 35, issue 9, pages 2318–2326, 2018. source code .
E. Bernard, L. Jacob, J. Mairal, E. Viara, and J-P. Vert. A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples. BMC Bioinformatics, volume 16, pages 262, 2015. source code.
E. Bernard, L. Jacob, J. Mairal and J.-P. Vert. Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows. Bioinformatics, 30(17), pages 2447–2455, 2014. source code.
Machine Learning for Neuroimaging
Dictionary learning and matrix factorization
A. Mensch, J. Mairal, B. Thirion and G. Varoquaux. Stochastic Subsampling for Factorizing Huge Matrices. IEEE Transactions on Signal Processing. 66(1), pages 113–128, 2018. source code .
A. Mensch, J. Mairal, B. Thirion and G. Varoquaux. Dictionary Learning for Massive Matrix Factorization. International Conference on Machine Learning (ICML), 2016. source code.
A. Tillmann, Y. C. Eldar, and J. Mairal. DOLPHIn-Dictionary Learning for Phase Retrieval. IEEE Transactions on Signal Processing, 64(24), pages 6485–6500, 2016. source code.
A. Tillmann, Y. C. Eldar, and J. Mairal. Dictionary Learning from Phaseless Measurements. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016. source code.
J. Mairal, M. Elad and F. Bach. Guest Editorial: Sparse Coding.
International Journal of Computer Vision (IJCV). 114(2-3), pages 89-90. 2015
Y. Chen, J. Mairal and Z. Harchaoui. Fast and Robust Archetypal Analysis for Representation Learning. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. source code. demo page.
J. Mairal, F. Bach and J. Ponce. Task-Driven Dictionary Learning. IEEE Pattern Analysis and Machine Intelligence (PAMI). 32(4). 2012.
L. Benoit, J. Mairal, F. Bach, J. Ponce, Sparse Image Representation with Epitomes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding. Journal of Machine Learning Research (JMLR), volume 11, pages 19-60, 2010. source code.
J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online Dictionary Learning for Sparse Coding. International Conference on Machine Learning (ICML), 2009. source code.
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Supervised Dictionary Learning. Advances Neural Information Processing Systems (NIPS), 2008.
J. Mairal, M. Leordeanu, F. Bach, M. Hebert and J. Ponce. Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation. European Conference on Computer Vision (ECCV), 2008.
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Discriminative Learned Dictionaries for Local Image Analysis. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
F. Bach, J. Mairal, J. Ponce, Convex Sparse Matrix Factorizations. Technical report HAL-00345747, 2008.
Structured sparse estimation
J. Mairal, B. Yu. Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows Journal of Machine Learning Research (JMLR), volume 14, pages 2449–2485, 2013. source code.
J. Mairal, B. Yu. Complexity Analysis of the Lasso Regularization Path. International Conference on Machine Learning (ICML), 2012. The video. source code.
J. Mairal, R. Jenatton, G. Obozinski and F. Bach. Convex and Network Flow Optimization for Structured Sparsity. Journal of Machine Learning Research (JMLR), volume 12, pages 2681–2720, 2011. source code.
R. Jenatton, J. Mairal, G. Obozinski and F. Bach. Proximal Methods for Hierarchical Sparse Coding. Journal of Machine Learning Research (JMLR), volume 12, pages 2297-2334, 2011. source code.
J. Mairal, R. Jenatton, G. Obozinski and F. Bach. Learning Hierarchical and Topographic Dictionaries with Structured Sparsity. In proceeding of the SPIE conference on wavelets and sparsity XIV. 2011.
J. Mairal, R. Jenatton, G. Obozinski and F. Bach. Network Flow Algorithms for Structured Sparsity. Adv. Neural Information Processing Systems (NIPS), 2010. source code.
R. Jenatton, J. Mairal, G. Obozinski and F. Bach. Proximal Methods for Sparse Hierarchical Dictionary Learning. International Conference on Machine Learning (ICML), 2010. source code.
Image processing
B. Lecouat, T. Eboli, J. Ponce and J. Mairal. High Dynamic Range and Super-Resolution From Raw Image Bursts. to appear at SIGGRAPH. 2022.
T. Bodrito, A. Zouaoui, J. Chanussot and J. Mairal. A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration. Adv. Neural Information Processing Systems (NeurIPS). 2021. source code
B. Lecouat, J. Ponce and J. Mairal. Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts. International Conference on Computer Vision (ICCV). 2021.
B. Lecouat, J. Ponce and J. Mairal. Designing and Learning Trainable Priors with Non-Cooperative Games. Adv. Neural Information Processing Systems (NeurIPS). 2020. source code
B. Lecouat, J. Ponce and J. Mairal. Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration. European Conference on Computer Vision (ECCV). 2020. source code
F. Couzinie-Devy, J. Mairal, F. Bach and J. Ponce. Dictionary Learning for Deblurring and Digital Zoom. technical report arXiv:1110.0957. 2011.
J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman. Non-Local Sparse Models for Image Restoration. International Conference on Computer Vision (ICCV), 2009. denoising software (binaries). demosaicking software (binaries).
J. Mairal, G. Sapiro and M. Elad. Learning multiscale sparse representations for image and video restoration. SIAM Multiscale Modeling and Simulation. 7(1), pages 214-241, 2008. source code (not maintained).
J. Mairal, M. Elad and G. Sapiro. Sparse representation for color image restoration. IEEE Transactions on Image Processing, 17(1), pages 53-69, 2008. The source code (not maintained).
J. Mairal, M. Elad, and G. Sapiro. Sparse Learned Representations for Image Restoration. 4th World conference of the IASC (International Association for Statistical Computing), invited paper, 2008. The source code (not maintained).
J. Mairal, G. Sapiro and M. Elad. Multiscale sparse image representation with learned dictionaries. In IEEE International Conference on Image Processing (ICIP), 2007. The source code (not maintained).
Natural Language Processing
Robotics
Thesis
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